AI Engineering & Architecture Review

Building AI systems is fundamentally different from traditional software. Get expert technical guidance on your AI implementation.

In this session, I'll review:

→ Your AI/ML architecture and tech stack choices

→ LLM integration patterns and agent frameworks

→ API design, error handling, and retry logic

→ Scalability, cost optimization, and performance considerations

→ Cloud infrastructure setup for AI workloads

→ Security concerns (prompt injection, data privacy)

→ Best practices for production AI systems

→ Testing and monitoring strategies

Perfect for: Engineering leads, CTOs, senior developers, and technical teams building AI features or products.

What makes this valuable: I don't just understand AI theory; I've built and shipped production AI agents at Canvas AI. I know what works in real-world scenarios with real users, not just in demos or research papers.

What you'll get:

1. 45-minute deep technical review
2. Specific, actionable recommendations
3. Architecture diagram feedback
4. Code review and optimization suggestions
5. Production deployment checklist
6. Follow-up resources

Come prepared with: Bring your architecture diagrams, code snippets, API designs, or technical questions. We'll focus on practical, implementable solutions that you can apply immediately.

Technologies I'm an expert in:

1. LLMs (OpenAI, Anthropic, open-source models)
2. Agent frameworks (LangChain, LlamaIndex, CrewAI)
3. Vector databases (Pinecone, Chroma, Weaviate)
4. Cloud platforms (AWS, GCP, Azure)
5. Python, Node.js, React, AI SDK for AI applications